Ekundayo E.O. Environmental monitoring

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Monitoring Information Systems to Support Adaptive Water Management

441

is, monitoring provides data to models, but the models are used to support the evaluation

and, eventually, the re-design of the monitoring network.

4.7 Policy evaluation

Theoretically speaking, the TIZIANO groundwater monitoring system should be capable of

supporting regional decision-makers at each step of the decisional path. In few words, the

network should support: 1) Assessing the initial state of the natural system and reporting

negative trends; 2) Controlling the effects of environmental actions and politics; 3) Alerting

for undesired evolutions.

The spatial extension of the monitoring network and the number of monitoring wells should

be revised at each step. Step one should be performed extensively over the monitoring area

and step two should focus around risk area. Step three should be suitably designed in order

to be capable of capturing any warning signal, at this step the position of the monitoring

points, the parameters to be measured and the frequency of measurement need to be

carefully evaluated. The TIZIANO monitoring network performed very well the first step

(Assess). Unlikely, we have less evaluation elements concerning the monitoring network

suitability during the second phase (Control). Finally, concerning the third step (Alert), the

monitoring activity have been moved and increased around area considered mostly at risk

and reduced in the rest of the region.

4.8 Monitoring evaluation

The monitoring program does not contain an evaluation phase. This means that the second

learning process described in figure 1 cannot be supported.

The critical analysis of the TIZIANO groundwater monitoring system can be summarized as

shown in table 3.

Criteria Evaluation

- Information producer/information

users interaction

- Mainl

based on scientific requirements

and legislation

- De

ree of participatio

- Weak in the desi

n phase, stron

durin

the implementation

- Multi-scale monitorin

- There is no anal

sis of inter-linka

between different scales

- Inte

ration of information sources - There is no inte

ratio

- Lon

time sustainabilit

- The monitorin

costs are too hi

- Monitorin

/modellin

interactio

- One-directional flow of information

- Polic

evaluatio

- The impacts on

roundwater are

monitored

- Monitorin

evaluatio

- The learnin

process is not supported

Table 3. Results of the evaluation

5. Conclusion

Starting from the results of the critical analysis, some drawbacks and potential

improvements for the TIZIANO monitoring program have been identified and discussed in

the following sections.

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5.1 Main drawbacks

According to the results of the critical analysis, we can infer that the TIZIANO groundwater

monitoring network cannot be considered as adaptive and it is not suitable to support the

adaptive management. Firstly, the excessive cost for collecting and analyzing data have a

strongly negative impact on the long term sustainability of the program. This, in turn,

would reduce the capability of the monitoring system to detect the long term unintended

consequences of the groundwater management policies.

Secondly, the monitoring system is not integrated in a wider program aiming to analyze the

different potential impacts of the policies – e.g. socio-economic impacts. The TIZIANO

monitoring program is based on the sectorial approach to environmental resources

management which is still common is socio-institutional contexts characterized by a

centralized and command-and-control regime. A more holistic and systemic approach is

required.

Thirdly, there is no integration between different sources of information. This has a negative

impact on the flexibility of the monitoring program. In fact, if the data collection is based

only on traditional “static” devices – i.e. monitoring stations – then the adaptation of the

monitoring program to modified information needs would be difficult: changing sensor is

not always easy and/or cheap, the position of the station cannot be modified easily, even

the time schedule for data collection cannot be changed easily. Although remote sensing

data are mentioned in the program, the integration of this source of data with the traditional

information sources is still far from being achieved.

Finally, an adaptive monitoring system requires an evaluation phase. That is, a critical

analysis of the suitability of the designed monitoring system is crucial. This phase has not

been considered in the current monitoring program. This means that the revision of the

program depends exclusively on the political willing of the local authorities and on the

availability of further funds.

5.2 Potential improvements

Some improvements to make the TIZIANO monitoring program more suitable to support

the adaptive water management were defined:

 Monitoring costs: the current monitoring costs could be reduced only if an intelligent

redistribution of activities within public institutions will be put in place. This means

that the outsourcing activities have to be strongly reduced. Moreover, since the costs are

mainly related to laboratories analysis, the integration of different sources of

information would have a positive impact on monitoring costs.

 Systemic analysis of the policy impacts: the increasing awareness of the complexity of

the real world forces us to adopt a system dynamic approach to monitor and analyze

the different and interrelated policy impacts. Although the aim of the TIZIANO

network is to collect data about the physical and chemical state of the groundwater, it

has to be integrated in a more systemic monitoring program, able to detect even the

socio-economical impacts.

 Integration between different sources of information: The integration of different

sources of knowledge seems particularly useful to design a multi – variate and multi –

scale monitoring system for adaptive management. The Use of alternative sources of

information increases the flexibility of monitoring program and reduce the costs.

Among the alternative sources of information, local knowledge is increasingly

considered as crucial (see as example the Hyogo Framework for Action). The analysis of

Monitoring Information Systems to Support Adaptive Water Management

443

the literature review on this issue allowed us gain some hints. The key to guarantee the

long term involvement of local community members in monitoring is to keep the

monitoring activities as simple and similar to the traditional methods for environmental

assessment as possible. Moreover, the involvement in monitoring is easier if the

monitoring activities are incorporated in the community members' daily activities. The

key to guarantee the actual usability of local knowledge in monitoring activities is: 1)

fully integrating local knowledge into existing traditional institutions; and 2)

structuring local knowledge so that it is transformed into meaningful and relevant

information for decision-making. The integration between local and scientific

knowledge allowed to enhance the reliability of local knowledge.

 Learning process in monitoring activities: as widely discussed in the scientific literature,

the design of a monitoring system cannot be considered as a linear process. It is rather a

cycle of design – implementation – evaluation – adaptation. The information needs can

change due to several reasons. Adaptive monitoring system should be able to follow

these changes. To this aim an evaluation phase should be formally included in the

monitoring program. The evaluation should be based on the interaction between policy

and decision makers (information users) and monitoring system managers (information

producers).

6. References

Bossel, H. (2001). Assessing viability and sustainability: a systems-based approach for

deriving comprehensive indicator sets. Conservation Ecology 5(2): 12. [online] URL:

http://www.consecol.org/vol5/iss2/art12/

Brock, W. A., and S. R. Carpenter (2006). Variance as a leading indicator of regime shift in

ecosystem services. Ecology and Society 11(2): 9. [online] URL:

http://www.ecologyandsociety.org/vol11/iss2/art9/

Campbell, B., J. A. Sayer, P. Frost, S. Vermeulen, M. Ruiz Pérez, A. Cunningham, and R.

Prabhu (2001). Assessing the performance of natural resource systems. Conservation

Ecology 5(2): 22. [online] URL: http://www.consecol.org/vol5/iss2/art22/

Checkland, P. (2001). Soft System Methodology. In Rational Analysis for a Problematic World.

Rosenhead, J., Mingers J. (eds), pp. 61-89. John Wiley and Sons, Chichester, UK.

Cofino, W.P. (1995). Quality management of monitoring programs. In Proceeding of the

international workshop on monitoring and assessment in water management; Monitoring

Tailor-Made. Adriaanse M., J van der Kraats; P.G. Stocks, and R.C. Wards (eds), 20-

23 September 1994, Beekbergen, The Netherlands.

Fazey, I., J.A. Fazey, and D.M.A. Fazey (2005). Learning More Effectively from Experience.

Ecology and Society, 10(2), 4. [online] URL:

http://www.ecologyandsociety.org/vol10/iss2/art4/

Holling, C.S. (ed.) (1978). Adaptive Environmental Assessment and Management. John Wiley

and Sons, New York.

Kolkman, M.J., M. Kok, A. van der Veen (2005). Mental model mapping as a new tool to

analyse the use of information in decision-making in integrated water

management. Physics and Chemistry of the Earth, 30: 317-332.

McIntosh, B S, Giupponi, C, Smith, C, Voinov, A, Matthews, K B, Monticino, M, Kolkman,

M J, Crossman, N, van Ittersum, M, Haase, D, Haase, A, Mysiak, J, Groot, J C J,

Sieber, S, Verweij, P, Quinn, N, Waeger, P, Gaber, N, Hepting, D, Scholten, H, Sulis,

Environmental Monitoring

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A, van Delden, H, Gaddis, E, Assaf, H. (2006). Bridging the gaps between design

and use: developing tools to support management and policy. In print.

Pahl-Wostl, C. (2007). The implications of complexity for integrated resources management.

Environmental Modelling and Software, 22: 561-569.

Smit, A.M. (2003). Adaptive monitoring: an overview. DOC Science Internal Series, vol. 138.

Department of Conservation, Wellington. 16 p.

Timmerman, J.G. and S. Langaas (2004). Conclusions. In Environmental information in

European transboundary water management. Timmerman, J.G. and S. Langaas (eds.),

pp. 240-246. IWA Publishing, London, UK. ISBN: 1 84339 038 8.

Timmerman, J.G., J.J. Ottens, and R.C. Ward (2000). The information cycle as a framework

for defining information goals for water-quality monitoring. Environmental

Management 25(3): 229-239.

Walker, B. H., L. H. Gunderson, A. P. Kinzig, C. Folke, S. R. Carpenter, and L. Schultz

(2006). A handful of heuristics and some propositions for understanding resilience

in social-ecological systems. Ecology and Society 11(1): 13. [online]

URL:http://www.ecologyandsociety.org/vol11/iss1/art13/

Walker, B. and J. A. Meyers (2004). Thresholds in ecological and social–ecological systems: a

developing database. Ecology and Society 9(2): 3. [online] URL:

http://www.ecologyandsociety.org/vol9/iss2/art3

Ward, R.C. (1995). Monitoring Tailor-made: what do you want to know? In Proceeding of the

international workshop on monitoring and assessment in water management; Monitoring

Tailor-Made. Adriaanse M., J van der Kraats; P.G. Stocks, and R.C. Wards (eds), 20-

23 September 1994, Beekbergen, The Netherlands.

Autonomous Decentralized Control Scheme for

Long-Term Operation of Large Scale and Dense

Wireless Sensor Networks with Multiple Sinks

Akihide Utani

Tokyo City University,

Japan

1. Introduction

Various communication services have been provided. They include environmental monitor-

ing and/or control, ad-hoc communication between mobile nodes, and inter-vehicle comm-

unication in intelligent transport systems. As a means of facilitating the above advanced co-

mmunication services, autonomous decentralized networks, such as wireless sensor networ-

ks (Akyildiz et al., 2002; Rajagopalan & Varshney, 2006), mobile ad-hoc networks (Perkins &

Royer, 1999; Johnson et al., 2003; Clausen & Jaquet, 2003; Ogier et al., 2003), and wireless me-

sh networks (Yamamoto et al., 2009), have been intensively researched with great interests.

Especially, a wireless sensor network, which is a key network to construct ubiquitous infor-

mation environments, has great potential as a means of realizing a wide range of applicatio-

ns, such as natural environmental monitoring, environmental control in residential spaces or

plants, object tracking, and precision agriculture (Akyildiz et al., 2002). Recently, there is gr-

owing expectation for a new network service by a wireless sensor network consisting of a lot

of static sensor nodes arranged in a service area and a few mobile robots as a result of the st-

rong desire for the development of advanced systems that can flexibly function in dynamic-

ally changing environments (Matsumoto et al., 2009).

In this chapter, a large scale and dense wireless sensor network made up of many static sen-

sor nodes with global positioning system, which is a representative network to actualize the

above-mentioned sensor applications, is assumed. In a large scale and dense wireless sensor

network, generally, hundreds or thousands of static sensor nodes limited resources, which

are compact and inexpensive, are placed in a service area, and sensing data of each node is

gathered to a sink node by inter-node wireless multi-hop communication. Each sensor node

consists of a sensing function to measure the status (temperature, humidity, motion, etc.) of

an observation point or object, a limited function of information processing, and a simplified

wireless communication function, and it generally operates on a resource with a limited po-

wer-supply capacity such as a battery. Therefore, a data gathering scheme and/or a routing

protocol capable of meeting the following requirements is mainly needed to prolong the life-

time of a large scale and dense wireless sensor network composed of hundreds or thousands

of static sensor nodes limited resources.

1. Efficiency of data gathering

2. Balance of communication load among sensor nodes

3. Adaptability to network topology changes

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As data gathering schemes for the long-term operation of a wireless sensor network,

cluster-ing-based data gathering (Heinzelman et al., 2000; Dasgupta et al., 2003; Jin et al.,

2008) and synchronization-based data gathering (Wakamiya & Murata, 2005; Nakano et

al., 2009; Nak-ano et al., 2011) are under study, but not all the above requirements are

satisfied. Recently, bio-inspired routing algorithms, such as ant-based routing algorithms,

have attracted a sign-ificant amount of interest from many researchers as examples that

satisfy the three require-ments above. In ant-based routing algorithms (Subramanian et

al., 1998; Ohtaki et al., 2006), the routing table of each sensor node is generated and

updated by applying the process in which ants build routes between their nest and food

using chemical substances (pheromon-es). Advanced ant-based routing algorithm (Utani

et al., 2008) is an efficient route learning algorithm which shares route information

between control messages. In contrast to conven-tional ant-based routing algorithms, this

can suppress the communication load of each sen-sor node and adapt itself to network

topology changes. However, this does not positively ease the communication load

concentration on specific sensor nodes, which is the source of problems in the long-term

operation of a wireless sensor network. Gradient-based routing protocol (Xia et al., 2004)

actualizes load-balancing data gathering. However, this cannot su-ppress the

communication load concentration to sensor nodes around the set sink node. Int-ensive

data transmission to specific sensor nodes results in concentrated energy consumpti-on by

them, and causes them to break away from the network early. This makes long-term

observation by a wireless sensor network difficult.

In a large scale and dense wireless sensor network, the communication load is generally co-

ncentrated on sensor nodes around the set sink node during the operation process. In cases

where sensor nodes are not placed evenly in a large scale observation area, the communica-

tion load is concentrated on sensor nodes placed in an area of low node density. To solve

this communication load concentration problem, a data gathering scheme for a wireless sen-

sor network with multiple sinks has been proposed (Dubois-Ferriere et al., 2004; Oyman &

Ersoy, 2004). In this scheme, each sensor node sends sensing data to the nearest sink node.

In comparison with the case of one-sink wireless sensor networks, the communication load

of sensor nodes around a sink node is reduced. In each sensor node, however, the destinati-

on sink node cannot be selected autonomously and adaptively. In cases where original data

transmission rate from each sensor node is not even, therefore, the load of load-concentrated

nodes is not sufficiently balanced. An autonomous load-balancing data transmission scheme

is required.

This chapter represents a new data gathering scheme with transmission power control that

adaptively reduces the load of load-concentrated nodes and facilitates the long-term operati-

on of a large scale and dense wireless sensor network with multiple sinks (Matsumoto et al.,

2010). This scheme has autonomous load-balancing data transmission devised by consider-

ing the application environment of a wireless sensor network as a typical example of compl-

ex systems where the adaptive adjustment of the entire system is realized from the local int-

eractions of components of the system. In this scheme, the load of each sensor node is auton-

omously balanced. This chapter consists of four sections. In Section 2, the above data gather-

ing scheme (Matsumoto et al., 2010) is detailed and its novelty and superiority are

described. In Section 3, the results of simulation experiments are reported and the

effectiveness of our scheme (Matsumoto et al., 2010) is demonstrated by comparing its

performances with those of existing schemes. In Section 4, the overall conclusions of this

work are given and future problems are discussed.

Autonomous Decentralized Control Scheme for Long-Term

Operation of Large Scale and Dense Wireless Sensor Networks with Multiple Sinks

447

2. Autonomous decentralized control scheme

To facilitate the long-term operation of an actual sensor network service, a recent approach

has been to introduce multiple sinks in a wireless sensor network (Dubois-Ferriere et al., 20-

04; Oyman & Ersoy, 2004). In a wireless sensor network with multiple sinks, sensing data of

each node is generally allowed to gather at any of the available sinks. Our scheme (Matsum-

oto et al., 2010) is a new data gathering scheme based on this assumption, which can be exp-

ected to produce a remarkable effect in a large scale and dense wireless sensor network with

multiple sinks. In our scheme, each sensor node can select either of high power and low po-

wer for packet transmission, where high power corresponds to normal transmission power

and low power is newly introduced to moreover balance the load of each sensor node.

2.1 Routing algorithm

Each sink node has a connective value named a “value to self”, which is not updated by tra-

nsmitting a control packet and receiving data packets. In the initial state of a large scale and

dense wireless sensor network with multiple sinks, each sink node broadcasts a control pac-

ket containing its own location information, ID, hop counts(=0), and “value to self” by high

power. This control packet is rebroadcast throughout the network with hop counts updated

by high power. By receiving the control packet from each sink node, each sensor node can

grasp the “value to self” of each sink node, their location information, IDs, and the hop cou-

nts from each sink node of its own neighborhood nodes.

Initial connective value of each sensor node, which is the connective value before starting

data transmission, is generated by using the “value to self” of each sink node and the hop

counts from each sink node. The procedure for computing initial connective value of a node

(i) is as follows:

1. The value [v

(0)] on each sink node (j=1, … ,S) of node (i) is first computed according to

the following equation

)1()( ,S,jdrvo0v

hops

jij

 　　

(1)

where vo

(j=1, … ,S) is the “value to self” of sink node (j), hops

(j=1, … ,S) is the hop

counts from sink node (j) of node (i). dr represents the value attenuation factor

accompanying the hop determined within the interval [0,1].

2. Then, initial connective value [v

(0)] of node (i) is generated by the following equation

),1,()(max)( S...j0v0v

iji



(2)

where this connective value [v

(0)] can be also conducted from the following equation

dr0vm0v





)()(

(3)

In the above Equation (3), vm

(0) represents the greatest connective value before starting

data transmission in neighborhood nodes of node (i).

Before data transmission is started, each sensor node computes initial connective value of

each neighborhood node based on the above Equations (1) and (2), and stores the

computed connective value, which is used as the only index to evaluate the relay

destination value of each neighborhood node, in each neighborhood node field of its own

routing table.

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2.2 Data transmission and connective value update

For a while from starting data transmission, each sensor node selects the neighboring node

with the greatest connective value from its own routing table as a relay node, and transmits

the data packet to this selected node by high power. In cases where more than one node sha-

res the greatest connective value, however, the relay node is determined between them at

random. The data packet in each sensor node is not sent to a specified sink node. By repetiti-

ve data transmission to the neighboring node with the greatest connective value, data gathe-

ring at any of the available sinks is completed. In our scheme, the connective value of each

sensor node is updated by considering residual node energy. Therefore, by repetitive data

transmission to the neighboring node with the greatest connective value, the data transmiss-

ion routes are not fixed.

To realize autonomous load-balancing data transmission, in our scheme (Matsumoto et al.,

2010), the data packet from each sensor node includes its own updated connective value. We

assume that a node (l) receives a data packet at time (t). Before node (l) relays the data pack-

et, it replaces the value in the connective value field of the data packet by its own renewal

connective value computed according to the following connective value update equation

drtvmtv

)(

)()( 

(4)

where vm

(t) is the greatest connective value at time (t) in the routing table of node (l). e

(t)

and E

represent the residual energy at time (t) of node (l) and the battery capacity of node

(l), respectively.

Data Packet

Next Hop

・・・

node l

・・・

(t)

・・・

node s routing table

・・・

Next Hop

・・・

node s

・・・

node l routing table

・・・

node r

(t)

Fig. 1. Data packet transmission and connective value update

In our scheme, the data packet addressed to the neighboring node with the greatest connect-

ive value is intercepted by all neighboring nodes. This data packet includes the updated co-

Autonomous Decentralized Control Scheme for Long-Term

Operation of Large Scale and Dense Wireless Sensor Networks with Multiple Sinks

449

nnective value of the source node based on the above Equation (4). Each neighborhood node

that intercepts this packet stores the updated connective value in the source node field of its

own routing table. Fig.1 shows an example of data packet transmission and its accompany-

ing connective value update. In this example, node (l) refers to its own routing table and ad-

dresses the data packet to node (r), which has the greatest connective value [vm

(t)]. When

this data packet is intercepted, each neighboring node around node (l) stores the updated

connective value [v

(t)] in the data packet in the node (l) field of its own routing table.

Sink1

: data packet

Sink2

・・・

(t)

・・・・・・

・・・

node xnode r

・・・

Next Hop

・・・

(t)

・・・・・・

・・・

node xnode r

・・・

Next Hop

node s routing table

Fig. 2. An example of autonomous load-balancing data transmission to multiple sinks

Our scheme (Matsumoto et al., 2010) requires the construction of a data gathering environm-

ent in the initial state of a large scale and dense wireless sensor network with multiple sinks,

but needs no special communication for network control. The above-mentioned simple mec-

hanism alone achieves autonomously adaptive load-balancing data transmission to multiple

sinks, as in Fig.2. The lifetime of a wireless sensor network can be extended by reducing the

communication load for network control and solving the node load concentration problem.

2.3 Transmission power control

For data packet transmission, the transmission power of each sensor node is switched to low

power if its own residual energy is less than the set threshold [T

]. In this case, each sensor

node selects the neighboring node with the greatest connective value within range of radio

wave of low power as a relay node, and transmits the data packet to this selected node by

low power.

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450

Sink1

: data packet: data packet

Next Hop

12.025.012.050.020.010.0

node snode rnode qnode nnode lnode

Next Hop

12.025.012.050.020.010.0

srqnlk

node m routing table

Fig. 3. An example of transmission power control

Fig.3 shows an example of the above transmission power control, which means that the

tra-nsmission power of each sensor node is switched to low power according to the above

con-dition. In this example, node (m) is a load concentration node. Node (m) has

autonomously transmitted the data packet to node (r) with the greatest connective value

within low power range by low power because its own residual energy has become less

than the set threshold [T

]. By switching to low power, the energy consumption of node

(m) is saved, but node (k) and node (l) may continue to transmit the data packet to node

(m) because they cannot grasp the updated connective value of node (m). In our scheme,

therefore, every tenth data packet from the node switched to low power is transmitted by

high power.

3. Simulation experiment

Through simulation experiments on a wireless sensor network with multiple sinks, the perf-

ormances of our scheme have been investigated in detail to verify its effectiveness.

3.1 Conditions of simulation

In a large scale and dense wireless sensor network with multiple sinks consisting of many

static sensor nodes placed in a large scale observation area, only sensor nodes that